To demonstrate our approach, we pretrain our model on long (30-second) video clips from Kinetics-700 [1]} and finetune it on various video classification tasks. One benefit of the modular design of our architecture is flexibility: once pretrained, we can use any of the subnetworks for downstream tasks depending on the modalities involved (audio-only, visual-only, audio-visual) and video lengths (short and long). To show this, we evaluate our model on UCF101 [2]} and ESC50 [3]} for short-term visual/audio classification, and Charades [4]} and Kinetics-Sounds [5]} for long-term audio-visual action recognition.
